We proposed a hybrid approach using the computational and statistical resources of the Bayesian Networks to learn a network structure from a data set using 4 different algorithms and the robustness of the statistical methods present in the Structural Equation Modeling to check the goodness of fit from model over data. We built an intermediate algorithm to join the features of 'bnlearn' and 'lavaan' R packages. The Bayesian Networks structure learning algorithms used were 'Hill-Climbing', 'Max-Min Hill-Climbing', 'Restricted Maximization' and 'Tabu Search'.
|Author||Elias Carvalho, Joao R N Vissoci, Luciano Andrade, Emerson P Cabrera, Julio C Nievola|
|Date of publication||2017-01-13 17:16:27|
|Maintainer||Elias Carvalho <[email protected]>|
|Package repository||View on CRAN|
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